Scikit-learn is a powerful machine learning library in Python that provides a range of tools for building and evaluating machine learning models. One of the key features of Scikit-learn is the ability to define custom scoring functions to evaluate the performance of a classifier based on user-defined criteria.
In this tutorial, we will walk through how to use Scikit-learn to build a classifier and define a custom scoring function that depends on a training feature. To do this, we will use a simple dataset to classify iris flowers into three different species.
Step 1: Import necessary libraries
First, we need to import the necessary libraries. We will be using the NumPy library for numerical operations and the pandas library for data manipulation.
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<body>import numpy as np
import pandas as pd
from sklearn.datasets import load_iris
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import make_scorer, accuracy_score
from sklearn.model_selection import cross_val_score
Step 2: Load the dataset
Next, we need to load the iris dataset from Scikit-learn.
# Load the iris dataset
iris = load_iris()
X = iris.data
y = iris.target
# Split the dataset into training and testing sets
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
Step 3: Define a custom scoring function
Now, let’s define a custom scoring function that depends on one of the training features. In this case, we will define a scoring function that computes the accuracy of the classifier on samples where the sepal length is greater than a specified threshold.
def sepal_length_accuracy(y_true, y_pred, threshold, X):
# Get indices of samples where sepal length is greater than the threshold
indices = np.where(X[:, 0] > threshold)
# Compute accuracy only on selected samples
y_true_filtered = y_true[indices]
y_pred_filtered = y_pred[indices]
return accuracy_score(y_true_filtered, y_pred_filtered)
Step 4: Create a custom scorer
Next, we need to create a custom scorer using the make_scorer function from Scikit-learn. We will pass in the custom scoring function we defined in the previous step and specify the threshold value.
# Define the threshold for sepal length
threshold = 6.5
# Create a custom scorer
custom_scorer = make_scorer(sepal_length_accuracy, threshold=threshold, X=X_train)
Step 5: Build and evaluate the classifier
Now, we can build a Random Forest classifier and use the custom scorer to evaluate its performance.
# Build a Random Forest classifier
clf = RandomForestClassifier(n_estimators=100)
# Evaluate the classifier using cross-validation with the custom scorer
scores = cross_val_score(clf, X_train, y_train, cv=5, scoring=custom_scorer)
print("Mean custom score:", np.mean(scores))
By following these steps, you can use Scikit-learn to build a classifier and define a custom scoring function that depends on a training feature. This allows you to evaluate the performance of the classifier based on your specific criteria and make more informed decisions when training machine learning models.
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